Artificial neural networks (ANNs) excel at pattern recognition and statistical modeling but struggle with tasks requiring explicit reasoning, context-independent generalization, or handling novel scenarios outside their training data. For example, ANNs cannot perform logical reasoning that involves step-by-step deduction or symbolic manipulation. While they can approximate solutions to problems like arithmetic through training, they lack the inherent ability to execute precise mathematical operations or follow algorithmic rules without extensive data. A neural network trained to add numbers might work for specific ranges but will fail to generalize beyond its training scope, unlike a simple calculator program that reliably follows predefined logic.
Another limitation is their inability to handle tasks requiring common-sense reasoning or real-world contextual understanding. For instance, ANNs might generate plausible-sounding text but often produce nonsensical or contradictory statements because they lack a grounded understanding of physics, social norms, or cause-and-effect relationships. A model trained on dialogue data might respond to “I’m hungry” with “Eat a sandwich” but could also suggest absurd actions like “Drink a chair” if the training data contains anomalies. This contrasts with humans, who use background knowledge to filter implausible options. Similarly, ANNs struggle with tasks like planning a sequence of actions in a dynamic environment (e.g., robot navigation) where outcomes depend on unobserved variables or abstract rules.
Finally, ANNs cannot inherently explain their decisions or provide transparent reasoning. While techniques like attention maps or feature visualization offer partial insights, the models themselves operate as “black boxes.” For example, a medical diagnosis ANN might correctly identify a tumor in an X-ray but cannot articulate the specific features (e.g., irregular borders) that led to its conclusion. This opacity makes them unreliable in high-stakes domains like healthcare or criminal justice, where accountability is critical. Additionally, ANNs require large amounts of labeled data for training, unlike humans who learn efficiently from few examples. A child can recognize a giraffe after seeing one picture, but an ANN needs thousands of labeled images to achieve similar accuracy, limiting its applicability in data-scarce scenarios.